With the wide popularization of Internet video-on-demand service and the rapid development of a video related technology, the related technology has been able to extract video watching behavior characteristics of a user from video playing data. The related technology collects, by embedding data collection codes into a video playing apparatus (video playing software), a count of a video played by any user within a unit time. Further, video playing counts of all users at each moment are summated, and the sum of playing counts at each moment is laid on a time axis of video playing, so a count of watching the video by a user at each moment may be presented. The watching behavior and favor degree of the user for video contents at a specific moment may be reflected by analyzing the count of watching the video by the user at each moment. However, since video playing counts of all users are only simply summated at each moment in the related art, video watching behaviors of the users cannot be specifically analyzed. For example, the count of cyclically playing a video by a user at a certain moment is extremely high, so the total count of playing the video by the user at this moment becomes large, thereby influencing the accuracy of analysis on the watching behavior and favor degree of the user for video contents at a specific moment. For another example, when a count of playing a video by a user at a certain moment is 0, the behavior of the user at this moment cannot be judged in the related art, that is, it cannot be judged whether the user completes video watching or the user skips over video contents at this moment.
Any effective solution has not been proposed yet at present for the problem in the related art where a video playing behavior cannot be accurately analyzed.